Prominent Feature Extraction for Sentiment Analysis:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham
Springer International Publishing
2016
|
| Ausgabe: | 1st ed. 2016 |
| Schriftenreihe: | Socio-Affective Computing
|
| Schlagwörter: | |
| Links: | https://doi.org/10.1007/978-3-319-25343-5 https://doi.org/10.1007/978-3-319-25343-5 https://doi.org/10.1007/978-3-319-25343-5 https://doi.org/10.1007/978-3-319-25343-5 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028962396&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028962396&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | 1 Online-Ressource (XIX, 103 p. 10 illus., 2 illus. in color) |
| ISBN: | 9783319253435 |
| DOI: | 10.1007/978-3-319-25343-5 |
Internformat
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Datensatz im Suchindex
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|---|---|
| adam_text | PROMINENT FEATURE EXTRACTION FOR SENTIMENT ANALYSIS
/ AGARWAL, BASANT
: 2016
TABLE OF CONTENTS / INHALTSVERZEICHNIS
INTRODUCTION
LITERATURE SURVEY
MACHINE LEARNING APPROACH FOR SENTIMENT ANALYSIS
SEMANTIC PARSING USING DEPENDENCY RULES
SENTIMENT ANALYSIS USING CONCEPTNET ONTOLOGY AND CONTEXT INFORMATION
SEMANTIC ORIENTATION BASED APPROACH FOR SENTIMENT ANALYSIS
CONCLUSIONS AND FUTUREWORK
REFERENCES
GLOSSARY
INDEX
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
PROMINENT FEATURE EXTRACTION FOR SENTIMENT ANALYSIS
/ AGARWAL, BASANT
: 2016
ABSTRACT / INHALTSTEXT
THE OBJECTIVE OF THIS MONOGRAPH IS TO IMPROVE THE PERFORMANCE OF THE
SENTIMENT ANALYSIS MODEL BY INCORPORATING THE SEMANTIC, SYNTACTIC AND
COMMON-SENSE KNOWLEDGE. THIS BOOK PROPOSES A NOVEL SEMANTIC CONCEPT
EXTRACTION APPROACH THAT USES DEPENDENCY RELATIONS BETWEEN WORDS TO
EXTRACT THE FEATURES FROM THE TEXT. PROPOSED APPROACH COMBINES THE
SEMANTIC AND COMMON-SENSE KNOWLEDGE FOR THE BETTER UNDERSTANDING OF THE
TEXT. IN ADDITION, THE BOOK AIMS TO EXTRACT PROMINENT FEATURES FROM THE
UNSTRUCTURED TEXT BY ELIMINATING THE NOISY, IRRELEVANT AND REDUNDANT
FEATURES. READERS WILL ALSO DISCOVER A PROPOSED METHOD FOR EFFICIENT
DIMENSIONALITY REDUCTION TO ALLEVIATE THE DATA SPARSENESS PROBLEM BEING
FACED BY MACHINE LEARNING MODEL. AUTHORS PAY ATTENTION TO THE FOUR MAIN
FINDINGS OF THE BOOK : -PERFORMANCE OF THE SENTIMENT ANALYSIS CAN BE
IMPROVED BY REDUCING THE REDUNDANCY AMONG THE FEATURES. EXPERIMENTAL
RESULTS SHOW THAT MINIMUM REDUNDANCY MAXIMUM RELEVANCE (MRMR) FEATURE
SELECTION TECHNIQUE IMPROVES THE PERFORMANCE OF THE SENTIMENT ANALYSIS
BY ELIMINATING THE REDUNDANT FEATURES. - BOOLEAN MULTINOMIAL NAIVE BAYES
(BMNB) MACHINE LEARNING ALGORITHM WITH MRMR FEATURE SELECTION TECHNIQUE
PERFORMS BETTER THAN SUPPORT VECTOR MACHINE (SVM) CLASSIFIER FOR
SENTIMENT ANALYSIS. - THE PROBLEM OF DATA SPARSENESS IS ALLEVIATED BY
SEMANTIC CLUSTERING OF FEATURES, WHICH IN TURN IMPROVES THE PERFORMANCE
OF THE SENTIMENT ANALYSIS. -SEMANTIC RELATIONS AMONG THE WORDS IN THE
TEXT HAVE USEFUL CUES FOR SENTIMENT ANALYSIS. COMMON-SENSE KNOWLEDGE IN
FORM OF CONCEPTNET ONTOLOGY ACQUIRES KNOWLEDGE, WHICH PROVIDES A BETTER
UNDERSTANDING OF THE TEXT THAT IMPROVES THE PERFORMANCE OF THE SENTIMENT
ANALYSIS
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
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| id | DE-604.BV043547033 |
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| indexdate | 2024-12-20T17:39:12Z |
| institution | BVB |
| isbn | 9783319253435 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028962396 |
| oclc_num | 934186702 |
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| physical | 1 Online-Ressource (XIX, 103 p. 10 illus., 2 illus. in color) |
| psigel | ZDB-2-SBL ZDB-2-SBL_2016_Fremddaten |
| publishDate | 2016 |
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| series2 | Socio-Affective Computing |
| spellingShingle | Agarwal, Basant Prominent Feature Extraction for Sentiment Analysis Medicine Neurosciences Data mining Text processing (Computer science) Application software Computational linguistics Biomedicine Document Preparation and Text Processing Computational Linguistics Data Mining and Knowledge Discovery Information Systems Applications (incl. Internet) Computer Appl. in Social and Behavioral Sciences Medizin |
| title | Prominent Feature Extraction for Sentiment Analysis |
| title_auth | Prominent Feature Extraction for Sentiment Analysis |
| title_exact_search | Prominent Feature Extraction for Sentiment Analysis |
| title_full | Prominent Feature Extraction for Sentiment Analysis by Basant Agarwal, Namita Mittal |
| title_fullStr | Prominent Feature Extraction for Sentiment Analysis by Basant Agarwal, Namita Mittal |
| title_full_unstemmed | Prominent Feature Extraction for Sentiment Analysis by Basant Agarwal, Namita Mittal |
| title_short | Prominent Feature Extraction for Sentiment Analysis |
| title_sort | prominent feature extraction for sentiment analysis |
| topic | Medicine Neurosciences Data mining Text processing (Computer science) Application software Computational linguistics Biomedicine Document Preparation and Text Processing Computational Linguistics Data Mining and Knowledge Discovery Information Systems Applications (incl. Internet) Computer Appl. in Social and Behavioral Sciences Medizin |
| topic_facet | Medicine Neurosciences Data mining Text processing (Computer science) Application software Computational linguistics Biomedicine Document Preparation and Text Processing Computational Linguistics Data Mining and Knowledge Discovery Information Systems Applications (incl. Internet) Computer Appl. in Social and Behavioral Sciences Medizin |
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